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Structural analysis of fuzzy controller with gaussian membership function
IFAC Proceedings Volumes, 1999Abstract Based on Mamdani's Max-Min inference operator, this paper presents an approach to the structure analysis of fuzzy controller with normal-distribution-shaped membership function. The authors deduce the output formulas of a typical fuzzy controller with linear control rales.
Xiang-Jie Liu, Xiao-Xin Zhou
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Approximation accuracy analysis of fuzzy systems as function approximators
IEEE Transactions on Fuzzy Systems, 1996This paper establishes the approximation error bounds for various classes of fuzzy systems (i.e., fuzzy systems generated by different inferential and defuzzification methods). Based on these bounds, the approximation accuracy of various classes of fuzzy systems is analyzed and compared.
null Xiao-Jun Zeng, M.G. Singh
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SOS-based fuzzy stability analysis via homogeneous Lyapunov functions
2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2014The class of polynomial fuzzy-model-based (PFMB) control systems has gained considerable attention in fuzzy control. The PFMB control system under consideration often assumes that the Lyapunov functions are quadratic, allowing use of semidefinite programming and the sum of squares (SOS) decomposition.
Ji-Chang Lo +3 more
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Stability analysis of fuzzy control systems using facet functions
Fuzzy Sets and Systems, 1995The authors point out that the stability and the robustness of control systems are highly desirable features that can only be demonstrated under strict conditions. One of these conditions is the existence of a known plant model. In cases when this model is available, conventional controllers are preferred, to fuzzy ones as they allow the demonstration ...
Kiendl, H., Rüger, J. J.
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Dynamic fuzzy data analysis based on similarity between functions
Fuzzy Sets and Systems, 1999Abstract In data analysis, objects are usually represented by feature vectors, each describing a state of an object at a point of time. Most methods for data analysis use only these feature vectors and do not take into account changes over time. They can therefore be called static.
A. Joentgen +3 more
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Quantification in Functional Magnetic Resonance Imaging: Fuzzy Clustering vs. Correlation Analysis
Magnetic Resonance Imaging, 1998The potential of functional MRI (fMRI) data analysis using the paradigm independent fuzzy cluster analysis (FCA) applied in the time domain compared to frequently used paradigm based correlation analysis (CA) was studied with simulated and in vivo fMRI data.
R, Baumgartner +2 more
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Describing function analysis of uncertain fuzzy vehicle control systems
Neural Computing and Applications, 2011In this paper, some useful frequency domain methods including describing function, parameter space, and Kharitonov approach are applied to analyze the stability of an uncertain fuzzy vehicle control system for limit cycle prediction. A systematic procedure is proposed to solve this problem. The fuzzy controller can be linearized by the use of classical
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Propositions for Fuzzy Characteristic Functions in Data Analysis
1982The author proposes a data coding technique using fuzzy characteristic functions, the construction of which depends on initial data. Application of a linear data analysis method to these coded data is (approximately) equivalent to the cor — responding non linear method applied to the initial data.
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Analysis of fuzzy -ary relations with the use of interval-valued fuzzy functional dependencies
International Journal of General Systems, 2013Abstract The paper contains an analysis of integrity constraints for -ary relations in fuzzy databases. Apart from dependencies between all attributes there may be also dependencies describing relationships of fewer attributes. However, there is no complete arbitrariness.
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